Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration o...
| 發表在: | Sensors |
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| Main Authors: | , , , , |
| 格式: | Article |
| 語言: | 英语 |
| 出版: |
MDPI AG
2022-09-01
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| 主題: | |
| 在線閱讀: | https://www.mdpi.com/1424-8220/22/17/6599 |
| _version_ | 1851846392226512896 |
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| author | Lei Hu Ligui Wang Yanlu Chen Niaoqing Hu Yu Jiang |
| author_facet | Lei Hu Ligui Wang Yanlu Chen Niaoqing Hu Yu Jiang |
| author_sort | Lei Hu |
| collection | DOAJ |
| container_title | Sensors |
| description | Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration of CEEMDAN, fault vibration signals are added with noises, both the vibration signals added with noises and the added noises are decomposed with classical empirical mode decomposition (EMD). This paper proposes a rolling bearing fault diagnosis method that combines piecewise aggregate approximation (PAA) with CEEMDAN. PAA enables CEEMDAN to decompose long signals and to achieve enhanced diagnosis. In particular, the method first yields the vibration envelope using bandpass filtering and demodulation, then compresses the envelope using PAA, and finally decomposes the compressed signal with CEEMDAN. Test data verification results show that the proposed method is more effective and more efficient than CEEMDAN. |
| format | Article |
| id | doaj-art-a1e9cecab9b64f51be27d4d4635efd61 |
| institution | Directory of Open Access Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-a1e9cecab9b64f51be27d4d4635efd612025-08-19T22:26:10ZengMDPI AGSensors1424-82202022-09-012217659910.3390/s22176599Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive NoiseLei Hu0Ligui Wang1Yanlu Chen2Niaoqing Hu3Yu Jiang4College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaLaboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, ChinaLaboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, ChinaComplete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration of CEEMDAN, fault vibration signals are added with noises, both the vibration signals added with noises and the added noises are decomposed with classical empirical mode decomposition (EMD). This paper proposes a rolling bearing fault diagnosis method that combines piecewise aggregate approximation (PAA) with CEEMDAN. PAA enables CEEMDAN to decompose long signals and to achieve enhanced diagnosis. In particular, the method first yields the vibration envelope using bandpass filtering and demodulation, then compresses the envelope using PAA, and finally decomposes the compressed signal with CEEMDAN. Test data verification results show that the proposed method is more effective and more efficient than CEEMDAN.https://www.mdpi.com/1424-8220/22/17/6599rolling bearingsfault diagnosispiecewise aggregate approximationCEEMDAN |
| spellingShingle | Lei Hu Ligui Wang Yanlu Chen Niaoqing Hu Yu Jiang Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise rolling bearings fault diagnosis piecewise aggregate approximation CEEMDAN |
| title | Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |
| title_full | Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |
| title_fullStr | Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |
| title_full_unstemmed | Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |
| title_short | Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |
| title_sort | bearing fault diagnosis using piecewise aggregate approximation and complete ensemble empirical mode decomposition with adaptive noise |
| topic | rolling bearings fault diagnosis piecewise aggregate approximation CEEMDAN |
| url | https://www.mdpi.com/1424-8220/22/17/6599 |
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